
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__lowerCAmelCase     :	List[Any]       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					enum.Enum	):


  '''simple docstring'''







  a__       			=						0
  a__       			=						1







@add_end_docstrings(_A	)
class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""generated"""
  def __init__(					self     :					Tuple     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					Optional[int]       )     ->      Dict:







    """simple docstring"""






    super().__init__(*UpperCamelCase__     ,				**UpperCamelCase__       )

    self.check_model_type(
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
        if self.framework == """tf"""
        else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING       )
  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					List[str]=None     ,				UpperCamelCase__     :					Union[str, Any]=None     ,				UpperCamelCase__     :					Dict=None     ,				UpperCamelCase__     :					Any=None     ,				UpperCamelCase__     :					List[Any]=None     ,				UpperCamelCase__     :					Tuple=None     ,				**UpperCamelCase__     :					List[str]     ,				)     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			{}
    if truncation is not None:
      __magic_name__								=			truncation

    __magic_name__								=			generate_kwargs

    __magic_name__								=			{}
    if return_tensors is not None and return_type is None:
      __magic_name__								=			ReturnType.TENSORS if return_tensors else ReturnType.TEXT
    if return_type is not None:
      __magic_name__								=			return_type

    if clean_up_tokenization_spaces is not None:
      __magic_name__								=			clean_up_tokenization_spaces

    if stop_sequence is not None:
      __magic_name__								=			self.tokenizer.encode(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__       )
      if len(UpperCamelCase__       ) > 1:
        warnings.warn(
            """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
            """ the stop sequence will be used as the stop sequence string in the interim."""       )
      __magic_name__								=			stop_sequence_ids[0]

    return preprocess_params, forward_params, postprocess_params
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int       )     ->      Tuple:







    """simple docstring"""






    return True
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					str     ,				UpperCamelCase__     :					int       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			self.model.config.prefix if self.model.config.prefix is not None else """"""
    if isinstance(args[0]     ,				UpperCamelCase__       ):
      if self.tokenizer.pad_token_id is None:
        raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""       )
      __magic_name__								=			([prefix + arg for arg in args[0]],)
      __magic_name__								=			True

    elif isinstance(args[0]     ,				UpperCamelCase__       ):
      __magic_name__								=			(prefix + args[0],)
      __magic_name__								=			False
    else:
      raise ValueError(
          F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''       )
    __magic_name__								=			self.tokenizer(*UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				return_tensors=self.framework       )
    # This is produced by tokenizers but is an invalid generate kwargs
    if "token_type_ids" in inputs:
      del inputs["token_type_ids"]
    return inputs
  def __call__(					self     :					Tuple     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			super().__call__(*UpperCamelCase__     ,				**UpperCamelCase__       )
    if (
        isinstance(args[0]     ,				UpperCamelCase__       )
        and all(isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) for el in args[0]       )
        and all(len(UpperCamelCase__       ) == 1 for res in result       )
    ):
      return [res[0] for res in result]
    return result
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					List[str]=TruncationStrategy.DO_NOT_TRUNCATE     ,				**UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			self._parse_and_tokenize(UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				**UpperCamelCase__       )
    return inputs
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					Any     ,				**UpperCamelCase__     :					Tuple       )     ->      Tuple:







    """simple docstring"""






    if self.framework == "pt":
      __magic_name__	,       __magic_name__								=			model_inputs["""input_ids"""].shape
    elif self.framework == "tf":
      __magic_name__	,       __magic_name__								=			tf.shape(model_inputs["""input_ids"""]       ).numpy()

    __magic_name__								=			generate_kwargs.get("""min_length"""     ,				self.model.config.min_length       )
    __magic_name__								=			generate_kwargs.get("""max_length"""     ,				self.model.config.max_length       )
    self.check_inputs(UpperCamelCase__     ,				generate_kwargs["""min_length"""]     ,				generate_kwargs["""max_length"""]       )
    __magic_name__								=			self.model.generate(**UpperCamelCase__     ,				**UpperCamelCase__       )
    __magic_name__								=			output_ids.shape[0]
    if self.framework == "pt":
      __magic_name__								=			output_ids.reshape(UpperCamelCase__     ,				out_b // in_b     ,				*output_ids.shape[1:]       )
    elif self.framework == "tf":
      __magic_name__								=			tf.reshape(UpperCamelCase__     ,				(in_b, out_b // in_b, *output_ids.shape[1:])       )
    return {"output_ids": output_ids}





  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					List[Any]=ReturnType.TEXT     ,				UpperCamelCase__     :					Optional[int]=False       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			[]
    for output_ids in model_outputs["output_ids"][0]:
      if return_type == ReturnType.TENSORS:
        __magic_name__								=			{F'''{self.return_name}_token_ids''': output_ids}
      elif return_type == ReturnType.TEXT:
        __magic_name__								=			{
            F'''{self.return_name}_text''': self.tokenizer.decode(
                UpperCamelCase__     ,				skip_special_tokens=UpperCamelCase__     ,				clean_up_tokenization_spaces=UpperCamelCase__     ,				)
        }
      records.append(UpperCamelCase__       )
    return records







@add_end_docstrings(_A	)
class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""summary"""
  def __call__(					self     :					Any     ,				*UpperCamelCase__     :					List[Any]     ,				**UpperCamelCase__     :					Dict       )     ->      str:







    """simple docstring"""






    return super().__call__(*UpperCamelCase__     ,				**UpperCamelCase__       )





  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int       )     ->      bool:







    """simple docstring"""






    if max_length < min_length:
      logger.warning(F'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''       )

    if input_length < max_length:
      logger.warning(
          F'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
          """a summarization task, where outputs shorter than the input are typically wanted, you might """
          F'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''       )







@add_end_docstrings(_A	)
class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""translation"""
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int       )     ->      Any:







    """simple docstring"""






    if input_length > 0.9 * max_length:
      logger.warning(
          F'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
          """increasing your max_length manually, e.g. translator('...', max_length=400)"""       )
    return True
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[str]=TruncationStrategy.DO_NOT_TRUNCATE     ,				UpperCamelCase__     :					List[str]=None     ,				UpperCamelCase__     :					int=None       )     ->      Union[str, Any]:







    """simple docstring"""






    if getattr(self.tokenizer     ,				"""_build_translation_inputs"""     ,				UpperCamelCase__       ):
      return self.tokenizer._build_translation_inputs(
          *UpperCamelCase__     ,				return_tensors=self.framework     ,				truncation=UpperCamelCase__     ,				src_lang=UpperCamelCase__     ,				tgt_lang=UpperCamelCase__       )
    else:
      return super()._parse_and_tokenize(*UpperCamelCase__     ,				truncation=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					Tuple=None     ,				UpperCamelCase__     :					List[str]=None     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    __magic_name__	,       __magic_name__	,       __magic_name__								=			super()._sanitize_parameters(**UpperCamelCase__       )
    if src_lang is not None:
      __magic_name__								=			src_lang
    if tgt_lang is not None:
      __magic_name__								=			tgt_lang
    if src_lang is None and tgt_lang is None:
      # Backward compatibility, direct arguments use is preferred.
      __magic_name__								=			kwargs.get("""task"""     ,				self.task       )
      __magic_name__								=			task.split("""_"""       )
      if task and len(UpperCamelCase__       ) == 4:
        # translation, XX, to YY
        __magic_name__								=			items[1]
        __magic_name__								=			items[3]
    return preprocess_params, forward_params, postprocess_params





  def __call__(					self     :					List[Any]     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    return super().__call__(*UpperCamelCase__     ,				**UpperCamelCase__       )



from __future__ import annotations

def a__       (			A_,	A_   ):






  '''simple docstring'''





  if b == 0:
    return (1, 0)
  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	a % b   )
  __magic_name__								=			a // b
  return (y, x - k * y)

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	A_   )
  __magic_name__								=			na * na
  __magic_name__								=			ra * x * na + ra * y * na
  return (n % m + m) % m

def a__       (			A_,	A_   ):






  '''simple docstring'''





  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	A_   )
  if b < 0:
    __magic_name__								=			(b % n + n) % n
  return b

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__	,       __magic_name__								=			invert_modulo(A_,	A_   ), invert_modulo(A_,	A_   )
  __magic_name__								=			na * na
  __magic_name__								=			ra * x * na + ra * y * na
  return (n % m + m) % m


if __name__ == "__main__":
    from doctest import testmod

    testmod(name='chinese_remainder_theorem', verbose=True)
    testmod(name='chinese_remainder_theorem2', verbose=True)
    testmod(name='invert_modulo', verbose=True)
    testmod(name='extended_euclid', verbose=True)

